NumPy #

We can use NumPy with

import numpy as np

We can create new arrays with

a = np.array([1, 5, 7])

We can specify number of dimensions or data types:

b = np.array([1, 2, 3], ndim=2)
c = np.array([1, 2, 3, 4], dtype=complex)

We can see the shape of an array via

print(a)
print(a.shape)

You can set the shape of an array

d = np.array([1, 2, 3], [4, 5, 6])
d.shape = (3, 2)

To create an array in a range

a = np.arange(24)

We can reshape this new array:

If we already have a Python list, we can create a NumPy array from it:

x = [1, 2, 3]
a = np.asarray(x)

Pandas #

• High performance data manipulation and data analysis
• DataFrame object, default and customized indexing
• High efficient merging and joining of data
• Columns from a data structure can be deleted or inserted
• Label based slicing, indexing of large datasets

Structures #

• Series, 1D labeled homogenous array
• DataFrame, 2D labeled hetergenously typed columns
• Panel, 3D labeled, size mutable array

Plotting #